AI Event and Wedding Photography API — Venue and Décor Variants
A wedding venue's marketing team has one photographer's gallery from last autumn's showcase — string lights, a specific floral arch, golden-hour light through the barn windows — and the sales deck needs to show the same room dressed for a winter evening reception, a spring daytime ceremony, and a corporate gala with a different color palette. Rebooking a photographer and a full styling crew for three more shoots of a room that already exists costs more than most venues or event planners budget for a single listing refresh, and the booking season doesn't wait for a styled re-shoot calendar to clear. Generating those variants from the existing gallery via API answers "what would the winter version look like" before any stylist is hired.
Direct answer: a reference-guided image generation API takes an existing venue or décor photo as input plus a prompt describing the new lighting, season, or color palette, and returns a new image that holds the room's architecture, camera angle, and composition fixed while changing only the described elements — no second shoot, no new styling budget.
How Does an API Generate Venue and Décor Variants From One Shoot?
Reference-guided generation anchors the output to the room's fixed geometry — walls, windows, ceiling height, furniture placement — then applies the lighting, season, or décor change described in the prompt. This differs from plain text-to-image generation, which has no source venue to match and would produce a plausible-looking room rather than the exact space a couple or corporate client is booking, a real risk in event marketing where a mismatch between the listing photo and the room that greets guests on the day drives complaints and refund requests.
As of October 2026, GenRelay exposes three image models through one endpoint for this workflow:
| Model | Best for | 1K price | 4K price | Editing support |
|---|---|---|---|---|
| Nano Banana Pro | Reference-guided scene changes (lighting, season, décor swap) | $0.030 | $0.042 | Yes, strong consistency |
| Nano Banana 2 | Faster batch variants, slightly lower consistency | $0.020 | $0.036 | Yes |
| GPT-image-2 | Instruction-based edits (remove/add an object, change a sign) | $0.014/image | $0.014/image | Yes, instruction-driven |
Nano Banana Pro holds architectural detail most reliably across a lighting or season change, which matters when the venue's actual windows and beam structure need to stay recognizable. GPT-image-2 is the better fit for small, targeted edits — swapping a table-number sign or removing a stray chair — rather than full scene restyling.
How Do I Call the API to Generate a Lighting Variant?
Authenticate with a bearer token and send the source photo plus a prompt describing the change. Here's a minimal working request using Nano Banana Pro:
import requests
API_KEY = "YOUR_KEY"
BASE = "https://genrelay.ai/v1"
with open("venue_autumn.jpg", "rb") as f:
r = requests.post(
f"{BASE}/images/generations",
headers={"Authorization": f"Bearer {API_KEY}"},
files={"reference_image": f},
data={
"model": "nano-banana-pro",
"prompt": (
"Same venue, same camera angle and architecture, "
"restyle for a winter evening reception: warm string "
"lights replaced with candlelight, deep red and gold "
"florals, snow visible through the windows."
),
"resolution": "2k",
},
)
job = r.json()
image_url = job["data"][0]["url"]
For a venue with multiple rooms or a planner with several color-palette options to pitch, batch the same reference image against a list of prompts rather than calling the endpoint one at a time:
import concurrent.futures
palettes = [
"winter evening: candlelight, deep red and gold florals, snow outside",
"spring daytime: natural light, pastel florals, greenery arch",
"corporate gala: cool blue uplighting, minimal florals, modern linens",
]
def generate(prompt):
with open("venue_autumn.jpg", "rb") as f:
r = requests.post(
f"{BASE}/images/generations",
headers={"Authorization": f"Bearer {API_KEY}"},
files={"reference_image": f},
data={"model": "nano-banana-pro", "prompt": prompt, "resolution": "2k"},
)
return r.json()["data"][0]["url"]
with concurrent.futures.ThreadPoolExecutor(max_workers=3) as pool:
results = list(pool.map(generate, palettes))
Both calls are synchronous — image generation returns a URL directly in the response rather than requiring a polling loop, unlike video generation jobs.
What Does a Full Venue Variant Set Cost?
Cost scales with resolution and image count, not with how different each variant looks from the source. A venue with 4 rooms needing 3 seasonal variants each — 12 images total — at Nano Banana Pro's 2K price of $0.030 per image costs $0.36 for the full set. Running the same 12 images at 4K for print-quality sales materials costs $0.50 at $0.042 per image. A corporate event planner generating 5 palette options across 8 past venue photos for a client pitch deck — 40 images — costs $1.20 at 2K.
| Scope | Images | Model/resolution | Total cost |
|---|---|---|---|
| Single room, 3 seasons | 3 | Nano Banana Pro, 2K | $0.09 |
| 4-room venue, 3 seasons each | 12 | Nano Banana Pro, 2K | $0.36 |
| Same set, print-quality | 12 | Nano Banana Pro, 4K | $0.50 |
| Client pitch deck, 8 venues × 5 palettes | 40 | Nano Banana Pro, 2K | $1.20 |
At this price point, generating a full seasonal or palette lineup costs less than a single hour of a venue coordinator's time, which is why most teams generate the full set rather than guessing which one option to commission a real shoot for.
How Do I Keep the Room Recognizable Across Variants?
Keep every prompt change scoped to lighting, season, décor, or color — never architecture, camera angle, or composition — since those are exactly the elements reference-guided generation is designed to hold fixed. If a variant starts drifting (a window moves, a ceiling beam disappears), the fix is almost always a prompt that's too aggressive about "reimagining" the space rather than restyling it; rephrase toward "same room, same angle, change only X."
FAQ
Does the API support video, not just photos, for venue walkthroughs?
Yes — GenRelay's video models (Veo 3.1 Lite, Grok Imagine) can turn a still venue photo into a short walkthrough-style clip. See the events and weddings video API guide for pricing and code.
Can I edit a specific object instead of restyling the whole scene?
Yes — GPT-image-2 handles instruction-based edits like "remove the exit sign" or "change the table numbers to gold" at $0.014 per image, cheaper than a full Nano Banana Pro restyle when the change is small.
Will the generated image pass as a real photo to a couple comparing it to the actual venue?
It should match the venue's real architecture and lighting conditions closely enough for marketing use, but disclose AI-generated marketing images per your local advertising rules — this is a legal, not technical, requirement.
What image formats does the API accept as a reference?
JPEG and PNG, up to 10MB. Larger source files should be downsized before upload; resolution of the output is controlled by the resolution parameter, not the input file size.
Is there a rate limit on reference-guided generation calls?
Yes, scoped per API key — check response headers for your current limit, and use a thread pool with a small max_workers count (3–5) for batch jobs rather than firing all requests simultaneously.
Explore the Nano Banana Pro model page for full parameter docs, or read the AI image editing API guide for more on GPT-image-2's instruction-based edits.